Bimetallic structures are used in a wide range of applications and play a key role in equipment such as aerospace and thermostats. However, the abundance of internal and external excitations and nonlinearities in bimetallic structures, as well as the complexity and variability of their operating environments, make their dynamics more complex to analyse compared to other systems. In addition, the interactions between the components that make up the bimetallic structure, wear and tear, and the operating environment will lead to uncertainties in the internal and external excitations and system parameters of the bimetallic structure. These uncertainties need to be taken into account in the dynamic analysis of bimetallic structures. At present, extensive research work has been carried out by scholars for the uncertainty analysis of bimetallic structures. We systematically review the current research status of the uncertainty dynamics of bimetallic structures by scholars at home and abroad in terms of the uncertainty in dynamics, beam dynamics, bolted joint dynamics, etc., and give the problems that need to be further investigated.
As a key component in mechanical transmission, straight-toothed bevel gears can realize commutation in the transmission process, withstand strong load capacity, which can meet the demand for efficient and stable transmission in engineering applications, so the study of its working characteristics has important engineering significance. The aim of this study is to analyze the static and contact modal analysis of the straight-toothed bevel gear pair by the finite element method to ensure the safety in the actual working process. Firstly, an accurate geometrical model of the straight bevel gear pair was established and meshed using finite element software. In the static analysis, the stress distribution and deformation of the gear pair under different loading conditions are considered. The strength of the gear pair was evaluated by applying different boundary conditions and loads. The contact modal analysis focuses on the influence of tooth contact characteristics on the dynamic response of the gear. The contact modes of the gear pair under actual working conditions are simulated, and the variation of the vibration pattern during its operation is analyzed and the corresponding frequency diagrams at different orders are listed, and it is found that the meshing frequency is lower than the hazardous frequency, and no danger will occur.
In industrial applications, detecting a fault in time is critical to ensure production safety. Edge-cloud collaborative condition monitoring provides a more flexible solution to achieve both computational efficiency and accuracy. In this paper, an Edge-cloud collaborative multi-level fault diagnosis model is developed based on stacked sparse autoencoder to minimize the fault detection time, meanwhile, the diagnostic accuracy can also be guaranteed. By filtering most of the normal data and less model inference time, the anomaly detection model on the edge can minimize the fault detection time. When a fault occurs, the fault data will be sent to the cloud to infer the fault details. The experimental results show that the proposed method can detect faults 0.12s earlier on average compared to edge-inferencing after cloud-trained method.
Edge-cloud collaboration provides a better solution for condition monitoring, which can reduce response time while maintaining computational efficiency. In practical condition monitoring scenarios, the individual differences among equipment often decrease the accuracy of diagnostic models. To tackle this problem, a transfer learning method based on stacked sparse autoencoder is proposed, which employs a data regularization strategy to improve feature extraction ability. The fault diagnosis model trained in the cloud transfers its model parameters and structure to the edge side. By a finetuning process with a small amount of data, and the model is further updated for condition monitoring of the individual machine. The experimental results show that the proposed KT-SAE method has improved transfer accuracy compared to other related transfer learning methods.
Based on the research on the dynamic test results of the piggyback transportation system, this paper analyzes the impact of the vibration acceleration generated during the operation of the semitrailer suspension system under different loading conditions on the safety of the piggyback transportation vehicle. The results show that the maximum lateral vibration acceleration of the semitrailer is about 0.3g, the maximum longitudinal acceleration is about 0.6g. The vibration acceleration of the semitrailer under full load is larger than that of the semitrailer under no-load conditions, and the value is approximately doubled. It shows that when the total load of semitrailer goods increases, the vibration of the suspension system will increase accordingly. If the overload occurs, it may affect the operational safety of the piggyback transportation system.
The influence of parameters on load distribution between teeth of HCR involute spur gear was studied. The load distribution was studied and verified theoretically by test. Under the condition of given parameter, the change curve of load distribution and maximum load sharing rate were studied when addendum coefficient, pressure angle, tooth number, modification coefficient and base pitch error respectively changed. The test result show that, under high loads, the maximum load sharing rate are linearly correlation to addendum coefficient, pressure angle, modification coefficient and base pitch error. With the increase of the number of teeth, the change rate of the maximum load sharing rate decreases from large to small, then tends to be flat. The rate of change in load sharing at the point of tooth engagement is small when the top height co-efficient, pressure angle, number of teeth and coefficient of variation change, and relatively large when the base joint deviation changes.
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